Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

Abstract

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.

Map-matching algorithm for large-scale low-frequency floating car data

“…In this study, probe vehicles were assumed to travel on the shortest path between two GPS points. In reality, travellers may choose the optimal path based on multiple criteria, such as travel time, number of turns and so on (Li et al 2011c, Ramaekers et al 2013). The incorporation of travellers' route choice behaviour is suggested as an interesting extension of the proposed algorithm, potentially improving the MM accuracy when the sampling time interval is large.…”

“…Using detailed spatiotemporal trajectories of probe vehicles, much recently, research effort has been devoted to a better understanding of people's behaviour, their interactions with each other and with the environment. For example, travellers' route choice set formulation (Rieser-Schüssler et al 2012, Ramaekers et al 2013, urban mobility patterns (González et al 2008, Liu et al 2012b) and accessibility patterns , Chen et al 2013d, commercial centre attractiveness (Yue et al 2012) and traffic emission estimation (Chang et al 2013).…”

A framework for automatically visualizing and recommending efficient routes

“…The first category addresses individual differences; particularly factors that are highly dependent on the driver's personal requirements, behavior and preferences. The choice for a specific route usually depends on the purpose of the trip (Ramaekers et al, 2013). Further factors address the driver's patience, familiarity with the area, flexibility, time pressure or the driver's experience.…”

Driver Behavior Modeling: Developments and Future Directions

“…(ii) Classification of literature according to the considered user preferences, for example, travel time, the number of intersections, traffic lights, and roadside aesthetics [56].…”

“…Genetic algorithms and ant colonization were also considered in several applications of route-finding problems in transportation networks. Another interesting literature review is available in [56] where Ramaekers et al classify the works according to the factors considered in the models such as travel time, number of intersections, traffic lights, roadside aesthetics, and several other factors. This study also investigates the relationship between the purpose of the trip and the road categories used.…”